code: def partition(
      global_rank, world_size, train_x, train_y, val_x, val_y
  ):
      # Partition training data
      data_per_rank = train_x.shape[0] // world_size
      idx_start = global_rank * data_per_rank
      idx_end = (global_rank + 1) * data_per_rank
      train_x = train_x[idx_start:idx_end]
      train_y = train_y[idx_start:idx_end]
  
      # Partition evaluation data
      data_per_rank = val_x.shape[0] // world_size
      idx_start = global_rank * data_per_rank
      idx_end = (global_rank + 1) * data_per_rank
      val_x = val_x[idx_start:idx_end]
      val_y = val_y[idx_start:idx_end]
      return train_x, train_y, val_x, val_y
  
imports: from singa import singa_wrap
  from singa import device
  from singa import tensor
  from singa import opt
  from singa import autograd
  from singa.opt import Optimizer
  from singa.opt import DecayScheduler
  from singa.opt import Constant
  import numpy
  import time
  import argparse
  from PIL import Image
label_desc: Use image node_id 3 for calling a global function with example usage: partition(global_rank, world_size, train_x, train_y, val_x, val_y) and returns: train_x,  train_y,  val_x,  val_y